Post-Approval Studies

Post-Approval Studies

In January 2005, the oversight responsibility of the Post-Approval Studies Program was transferred to the Division of Epidemiology (DEPI) of the Office of Surveillance and Biometrics (OSB)/Center for Devices and Radiological Health (CDRH).

The CDRH Post-Approval Studies Program encompasses design, tracking, oversight, and review responsibilities for studies mandated as a condition of approval of a premarket approval (PMA) application, protocol development product (PDP) application, or humanitarian device exemption (HDE) application. The program helps ensure that well-designed post-approval studies (PAS) are conducted effectively and efficiently and in the least burdensome manner.

CDRH has established an automated, internal tracking system that efficiently identifies the reporting status of active PAS studies ordered since January 1, 2005 based on study timelines incorporated in study protocols and agreed upon by the CDRH and applicants. This system represents CDRH's effort to ensure that all PAS commitments are fulfilled in a timely manner.

In addition, CDRH launched this publicly available webpage to keep all stakeholders informed of the progress of each PAS. The webpage displays general information regarding each PAS, as well as the overall study status (based on protocol-driven timelines and the adequacy of the data) and the applicant's reporting status for each submission due.

The post-approval study is a prospective, non-randomized, combined cohort of subjects followed in two other studies. Data from the combined cohort are used prospectively to assess performance of the algorithm.
The post-approval study is a prospective, non-randomized, combined cohort of subjects followed in two other

studies. Data from the combined cohort are used prospectively to assess performance of the algorithm.

Study Population Description

Study population same as approved product. This device is a software algorithm designed to identify potential RV-Tip to RV-Ring pace/sense electrode issues earlier than contemporary out-of-range impedance criteria. The RVLIA algorithm uses two different criteria to determine potential lead noise. It uses data from the ventricular sensing integrity counter (SIC) and non-sustained VT episodes (NST) to determine potential noise and also looks at the RV pacing lead impedance values.
Study population same as approved product. This device is a software algorithm designed to identify

potential RV-Tip to RV-Ring pace/sense electrode issues earlier than contemporary out-of-range impedance criteria. The RVLIA algorithm uses two different criteria to determine potential lead noise. It uses data from the ventricular sensing integrity counter (SIC) and non-sustained VT episodes (NST) to determine potential noise and also looks at the RV pacing lead impedance values.

Sample Size

3873 subjects from multiple centers in 2 cohorts

Data Collection

Data are collected via review of the databases using the device algorithm. Triggers associated with the algorithm are reviewed. Lead fractures are confirmed.
Data are collected via review of the databases using the device algorithm. Triggers associated with

Of 47 LIA triggers, there were eight false-positive alerts increase without lead modification as indicated in the Device Registration System. . The false-positives included two gradually increasing impedance, two T-wave oversensing cases, and one each caused by a header-connector problem, hypothermic ventricular fibrillation during cardiac arrest at cardiac surgery, ventricular fibrillation storm, and one case adjudicated as unknown with abrupt impedance. The estimated number of false positive LIA triggers based on the retrospective analysis was 0.00269 per patient-year.

Of 41 events, 39 were detected by LIA algorithm, giving an estimated sensitivity of 95.1%

(95% IC 83.5%-99.4). The inappropriate shock rate was 21%.

Of 47 LIA triggers, there were eight false-positive alerts increase without lead modification as indicated in the Device Registration System. . The false-positives included two gradually increasing impedance, two T-wave oversensing cases, and one each caused by a header-connector problem, hypothermic ventricular fibrillation during cardiac arrest at cardiac surgery, ventricular fibrillation storm, and one case adjudicated as unknown with abrupt impedance. The estimated number of false positive LIA triggers based on the retrospective analysis was 0.00269 per patient-year.

Study Strengths and Weaknesses

Not all products were returned for the Return Product Analysis. Of 47 LIA triggers, 21 (47%) of the products were not analyzed, where 15 were not returned and 6 were listed as ¿no record¿.
Not all products were returned for the Return Product Analysis. Of 47 LIA triggers, 21

(47%) of the products were not analyzed, where 15 were not returned and 6 were listed as ¿no record¿.

Recommendations for Labeling Changes

Labeling changes should be accomplished in accordance with the findings of the PAS.